






from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef

import datasets


UpperCamelCase								=		"\\n@inproceedings{wang2019glue,\n  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n  note={In the Proceedings of ICLR.},\n  year={2019}\n}\n"

UpperCamelCase								=		"\\nGLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n"

UpperCamelCase								=		"\nCompute GLUE evaluation metric associated to each GLUE dataset.\nArgs:\n    predictions: list of predictions to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\nReturns: depending on the GLUE subset, one or several of:\n    \"accuracy\": Accuracy\n    \"f1\": F1 score\n    \"pearson\": Pearson Correlation\n    \"spearmanr\": Spearman Correlation\n    \"matthews_correlation\": Matthew Correlation\nExamples:\n\n    >>> glue_metric = datasets.load_metric('glue', 'sst2')  # 'sst2' or any of [\"mnli\", \"mnli_mismatched\", \"mnli_matched\", \"qnli\", \"rte\", \"wnli\", \"hans\"]\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0}\n\n    >>> glue_metric = datasets.load_metric('glue', 'mrpc')  # 'mrpc' or 'qqp'\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0, 'f1': 1.0}\n\n    >>> glue_metric = datasets.load_metric('glue', 'stsb')\n    >>> references = [0., 1., 2., 3., 4., 5.]\n    >>> predictions = [0., 1., 2., 3., 4., 5.]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print({\"pearson\": round(results[\"pearson\"], 2), \"spearmanr\": round(results[\"spearmanr\"], 2)})\n    {'pearson': 1.0, 'spearmanr': 1.0}\n\n    >>> glue_metric = datasets.load_metric('glue', 'cola')\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'matthews_correlation': 1.0}\n"






def    __magic_name__    (	SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)     ->    int:
							return float((preds == labels).mean()				)






def    __magic_name__    (	SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)     ->    Optional[Any]:
							_lowercase							:   Optional[int] 							=   simple_accuracy(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)
							_lowercase							:   int 							=   float(fa_score(y_true=SCREAMING_SNAKE_CASE     ,     y_pred=SCREAMING_SNAKE_CASE				)				)
							return {
							    "accuracy": acc,
							    "f1": fa,
							}






def    __magic_name__    (	SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)     ->    Any:
							_lowercase							:   Optional[Any] 							=   float(pearsonr(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)[0]				)
							_lowercase							:   int 							=   float(spearmanr(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)[0]				)
							return {
							    "pearson": pearson_corr,
							    "spearmanr": spearman_corr,
							}






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION   ,		_KWARGS_DESCRIPTION			)
class 				lowerCAmelCase_	(		datasets.Metric			):


				def 						__a					(					self      ):
											if self.config_name not in [
											    "sst2",
											    "mnli",
											    "mnli_mismatched",
											    "mnli_matched",
											    "cola",
											    "stsb",
											    "mrpc",
											    "qqp",
											    "qnli",
											    "rte",
											    "wnli",
											    "hans",
											]:
																		raise KeyError(
																		    'You should supply a configuration name selected in '
																		    '["sst2", "mnli", "mnli_mismatched", "mnli_matched", '
																		    '"cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans"]'      )
											return datasets.MetricInfo(
											    description=_DESCRIPTION ,						citation=_CITATION ,						inputs_description=_KWARGS_DESCRIPTION ,						features=datasets.Features(
											        {
											            'predictions': datasets.Value('int64' if self.config_name != 'stsb' else 'float32'      ),
											            'references': datasets.Value('int64' if self.config_name != 'stsb' else 'float32'      ),
											        }      ) ,						codebase_urls=[] ,						reference_urls=[] ,						format='numpy' ,						)


				def 						__a					(					self ,						_lowerCAmelCase ,						_lowerCAmelCase      ):
											if self.config_name == "cola":
																		return {"matthews_correlation": matthews_corrcoef(_lowerCAmelCase ,						_lowerCAmelCase      )}
											elif self.config_name == "stsb":
																		return pearson_and_spearman(_lowerCAmelCase ,						_lowerCAmelCase      )
											elif self.config_name in ["mrpc", "qqp"]:
																		return acc_and_fa(_lowerCAmelCase ,						_lowerCAmelCase      )
											elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
																		return {"accuracy": simple_accuracy(_lowerCAmelCase ,						_lowerCAmelCase      )}
											else:
																		raise KeyError(
																		    'You should supply a configuration name selected in '
																		    '["sst2", "mnli", "mnli_mismatched", "mnli_matched", '
																		    '"cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans"]'      )






import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


lowercase    :   Tuple						=			logging.getLogger(__name__)




class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	def __init__(   self						:Dict	,  a						:Union[str, Any]=-1		)						->					List[str]:
								# in NER datasets, the last column is usually reserved for NER label
								__UpperCamelCase      :  str       =   label_idx







	def   _lowerCamelCase							(   self						:str	,  a						:Tuple	,  a						:Union[Split, str]		)						->					List[InputExample]:
								if isinstance(a	,  a		):
															__UpperCamelCase      :  Dict       =   mode.value
								__UpperCamelCase      :  Union[str, Any]       =   os.path.join(a	,  f'{mode}.txt'		)
								__UpperCamelCase      :  Any       =   1
								__UpperCamelCase      :  List[str]       =   []
								with open(a	,  encoding="utf-8"		) as f:
															__UpperCamelCase      :  Tuple       =   []
															__UpperCamelCase      :  Dict       =   []
															for line in f:
																						if line.startswith("-DOCSTART-"		) or line == "" or line == "\n":
																													if words:
																																				examples.append(InputExample(guid=f'{mode}-{guid_index}'	,  words=a	,  labels=a		)		)
																																				guid_index += 1
																																				__UpperCamelCase      :  Dict       =   []
																																				__UpperCamelCase      :  List[str]       =   []
																						else:
																													__UpperCamelCase      :  Union[str, Any]       =   line.split(" "		)
																													words.append(splits[0]		)
																													if len(a		) > 1:
																																				labels.append(splits[self.label_idx].replace("\n"	,  ""		)		)
																													else:
																																				# Examples could have no label for mode = "test"
																																				labels.append("O"		)
															if words:
																						examples.append(InputExample(guid=f'{mode}-{guid_index}'	,  words=a	,  labels=a		)		)
								return examples







	def   _lowerCamelCase							(   self						:int	,  a						:TextIO	,  a						:TextIO	,  a						:List		)						->					Optional[Any]:
								__UpperCamelCase      :  Tuple       =   0
								for line in test_input_reader:
															if line.startswith("-DOCSTART-"		) or line == "" or line == "\n":
																						writer.write(a		)
																						if not preds_list[example_id]:
																													example_id += 1
															elif preds_list[example_id]:
																						__UpperCamelCase      :  Union[str, Any]       =   line.split()[0] + " " + preds_list[example_id].pop(0		) + "\n"
																						writer.write(a		)
															else:
																						logger.warning("Maximum sequence length exceeded: No prediction for '%s'."	,  line.split()[0]		)







	def   _lowerCamelCase							(   self						:Tuple	,  a						:str		)						->					List[str]:
								if path:
															with open(a	,  "r"		) as f:
																						__UpperCamelCase      :  List[str]       =   f.read().splitlines()
															if "O" not in labels:
																						__UpperCamelCase      :  Any       =   ["O"] + labels
															return labels
								else:
															return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]




class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	def __init__(   self						:List[str]		)						->					int:
								# in CONLL2003 dataset chunk column is second-to-last
								super().__init__(label_idx=-2		)







	def   _lowerCamelCase							(   self						:List[str]	,  a						:str		)						->					List[str]:
								if path:
															with open(a	,  "r"		) as f:
																						__UpperCamelCase      :  Optional[Any]       =   f.read().splitlines()
															if "O" not in labels:
																						__UpperCamelCase      :  int       =   ["O"] + labels
															return labels
								else:
															return [
															    "O",
															    "B-ADVP",
															    "B-INTJ",
															    "B-LST",
															    "B-PRT",
															    "B-NP",
															    "B-SBAR",
															    "B-VP",
															    "B-ADJP",
															    "B-CONJP",
															    "B-PP",
															    "I-ADVP",
															    "I-INTJ",
															    "I-LST",
															    "I-PRT",
															    "I-NP",
															    "I-SBAR",
															    "I-VP",
															    "I-ADJP",
															    "I-CONJP",
															    "I-PP",
															]




class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	def   _lowerCamelCase							(   self						:List[str]	,  a						:Union[str, Any]	,  a						:Union[Split, str]		)						->					List[InputExample]:
								if isinstance(a	,  a		):
															__UpperCamelCase      :  Optional[Any]       =   mode.value
								__UpperCamelCase      :  List[str]       =   os.path.join(a	,  f'{mode}.txt'		)
								__UpperCamelCase      :  Dict       =   1
								__UpperCamelCase      :  List[str]       =   []

								with open(a	,  encoding="utf-8"		) as f:
															for sentence in parse_incr(a		):
																						__UpperCamelCase      :  Optional[int]       =   []
																						__UpperCamelCase      :  int       =   []
																						for token in sentence:
																													words.append(token["form"]		)
																													labels.append(token["upos"]		)
																						assert len(a		) == len(a		)
																						if words:
																													examples.append(InputExample(guid=f'{mode}-{guid_index}'	,  words=a	,  labels=a		)		)
																													guid_index += 1
								return examples







	def   _lowerCamelCase							(   self						:List[Any]	,  a						:TextIO	,  a						:TextIO	,  a						:List		)						->					str:
								__UpperCamelCase      :  List[Any]       =   0
								for sentence in parse_incr(a		):
															__UpperCamelCase      :  Tuple       =   preds_list[example_id]
															__UpperCamelCase      :  Dict       =   ""
															for token in sentence:
																						out += f'{token["form"]} ({token["upos"]}|{s_p.pop(0		)}) '
															out += "\n"
															writer.write(a		)
															example_id += 1







	def   _lowerCamelCase							(   self						:List[str]	,  a						:str		)						->					List[str]:
								if path:
															with open(a	,  "r"		) as f:
																						return f.read().splitlines()
								else:
															return [
															    "ADJ",
															    "ADP",
															    "ADV",
															    "AUX",
															    "CCONJ",
															    "DET",
															    "INTJ",
															    "NOUN",
															    "NUM",
															    "PART",
															    "PRON",
															    "PROPN",
															    "PUNCT",
															    "SCONJ",
															    "SYM",
															    "VERB",
															    "X",
															]